A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation

In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called weighted optimization framework is intended to serve as a generic method that can be used with any population-based metaheuristic algorithm. A...

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Vydáno v:	IEEE Transactions on Evolutionary Computation Ročník 22; číslo 2; s. 260 - 275
Hlavní autoři:	Zille, Heiner, Ishibuchi, Hisao, Mostaghim, Sanaz, Nojima, Yusuke
Médium:	Journal Article
Jazyk:	angličtina japonština
Vydáno:	IEEE 01.04.2018 Institute of Electrical and Electronics Engineers (IEEE)
Témata:	Algorithm design and analysis Benchmark testing Computer science Diversity reception Electronic mail Large-scale optimization (LSO) many-variable optimization metaheuristic framework multiobjective optimization Optimization Search problems variable grouping weighting
ISSN:	1089-778X, 1941-0026
On-line přístup:	Získat plný text
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Abstract	In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called weighted optimization framework is intended to serve as a generic method that can be used with any population-based metaheuristic algorithm. After explaining some general issues of large-scale optimization, we introduce a problem transformation scheme that is used to reduce the dimensionality of the search space and search for improved solutions in the reduced subspace. This involves so-called weights that are applied to alter the decision variables and are also subject to optimization. Our method relies on grouping mechanisms and employs a population-based algorithm as an optimizer for both original variables and weight variables. Different grouping mechanisms and transformation functions within the framework are explained and their advantages and disadvantages are examined. Our experiments use test problems with 2-3 objectives 40-5000 variables. Using our approach on three well-known algorithms and comparing its performance with other large-scale optimizers, we show that our method can significantly outperform most existing methods in terms of solution quality as well as convergence rate on almost all tested problems for many-variable instances.
AbstractList	In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called weighted optimization framework is intended to serve as a generic method that can be used with any population-based metaheuristic algorithm. After explaining some general issues of large-scale optimization, we introduce a problem transformation scheme that is used to reduce the dimensionality of the search space and search for improved solutions in the reduced subspace. This involves so-called weights that are applied to alter the decision variables and are also subject to optimization. Our method relies on grouping mechanisms and employs a population-based algorithm as an optimizer for both original variables and weight variables. Different grouping mechanisms and transformation functions within the framework are explained and their advantages and disadvantages are examined. Our experiments use test problems with 2-3 objectives 40-5000 variables. Using our approach on three well-known algorithms and comparing its performance with other large-scale optimizers, we show that our method can significantly outperform most existing methods in terms of solution quality as well as convergence rate on almost all tested problems for many-variable instances.
Author	Mostaghim, Sanaz Ishibuchi, Hisao Nojima, Yusuke Zille, Heiner
Author_xml	– sequence: 1 givenname: Heiner orcidid: 0000-0002-7262-9487 surname: Zille fullname: Zille, Heiner email: heiner.zille organization: Institute for Intelligent Cooperating Systems, Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany – sequence: 2 givenname: Hisao orcidid: 0000-0001-9186-6472 surname: Ishibuchi fullname: Ishibuchi, Hisao email: hisao@sustc.edu.cn organization: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China – sequence: 3 givenname: Sanaz orcidid: 0000-0002-9917-5227 surname: Mostaghim fullname: Mostaghim, Sanaz email: sanaz.mostaghim@ovgu.de organization: Institute for Intelligent Cooperating Systems, Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany – sequence: 4 givenname: Yusuke orcidid: 0000-0003-4853-1305 surname: Nojima fullname: Nojima, Yusuke email: nojima@cs.osakafu-u.ac.jp organization: Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan
BackLink	https://cir.nii.ac.jp/crid/1870302167732148736$$DView record in CiNii
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Snippet	In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called...
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SubjectTerms	Algorithm design and analysis Benchmark testing Computer science Diversity reception Electronic mail Large-scale optimization (LSO) many-variable optimization metaheuristic framework multiobjective optimization Optimization Search problems variable grouping weighting
Title	A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation
URI	https://ieeexplore.ieee.org/document/7929324 https://cir.nii.ac.jp/crid/1870302167732148736
Volume	22
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